jupyter-editor-mcp
Server Quality Checklist
Latest release: v0.2.2
- Disambiguation3/5
Most tools have clear boundaries, but there is notable overlap: ipynb_read_notebook and ipynb_get_notebook_info both return nearly identical notebook summaries, and ipynb_apply_to_notebooks duplicates operations already available via dedicated tools (e.g., set_kernel, clear_outputs, update_metadata). Batch vs single versions are distinct but admirably named.
Naming Consistency5/5All tool names follow a consistent ipynb_ verb_noun pattern, with descriptive verbs like list, get, insert, delete, replace, merge, validate. Even compound verbs like str_replace and search_replace are clear and predictable, and batch variants consistently append '_batch'.
Tool Count2/5At 29 tools, this exceeds the 'too many (25+)' threshold. While the domain is rich, the count feels bloated due to the inclusion of both single and batch versions of many operations, plus near-duplicate read/info tools. A leaner set around 20 would be more appropriate.
Completeness4/5The tool set covers cell CRUD, notebook metadata, kernel settings, validation, searching, and multi-notebook operations like merge, split, and batch processing. Minor gaps exist, such as no explicit 'create new notebook' tool, but agents can work around this via merge or extract flows.
Average 3.9/5 across 29 of 29 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility. It mentions the merge option and return structure, but fails to disclose whether it modifies files in place, the default behavior when merge is false, or error handling for partial failures. This is insufficient for a batch mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with distinct Args and Returns sections. Every sentence provides essential information, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers what it does, its parameters, and its return value, which is good given the output schema. However, it lacks important context such as which metadata level (notebook vs cell) is affected, the exact merge semantics, and partial failure behavior. This is a noticeable gap for a batch operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no parameter descriptions, and the description's Args section adds some meaning: 'absolute paths preferred' for ipynb_filepaths and 'merge with existing' for merge. However, 'metadata: Metadata to apply' is tautological and leaves the metadata structure underspecified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Synchronize metadata across multiple Jupyter Notebooks (.ipynb)', identifying the action (synchronize metadata) and resource (multiple notebooks). It implies batch usage, distinguishing itself from single-notebook siblings like ipynb_update_metadata, though 'synchronize' is somewhat vague without reading the Args.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use guidance or alternatives are mentioned. The phrase 'across multiple' hints at batch use, but there is no differentiation from sibling tools like ipynb_apply_to_notebooks or ipynb_update_metadata, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only mentions the return format ('Dict with 'success' or 'error' key') but omits side effects like in-place file modification, overwriting of existing kernel metadata, or validation of the kernel name. This under-discloses for a mutating operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured as a concise docstring with purpose, arguments, and return value. It front-loads the core action and contains no filler, though it has minor whitespace. Overall efficient and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, but the description omits usage context and side effects. For a mutating tool, an agent may not realize the notebook file is modified or need to check available kernels beforehand. Referencing sibling tools like ipynb_list_available_kernels would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description compensates by listing all four parameters with brief explanations, including examples for kernel_name and display_name and a default for language. However, it lacks deeper semantics such as allowed values, constraints, or parameter relationships.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Set kernel specification for a Jupyter Notebook (.ipynb).' This uses a specific verb and resource, distinguishing it from sibling tools like ipynb_list_available_kernels or ipynb_update_metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. It does not mention prerequisites such as kernel availability, nor does it contrast with related tools like ipynb_list_available_kernels. The usage is implied by the action but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral implications. It says 'delete others,' indicating destructive action, but does not clarify whether the file is modified in-place, what happens if no filters are given (keep all or delete all), or any prerequisites/backup considerations. This is a significant transparency gap for a destructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured, with a front-loaded one-line summary followed by an Args/Returns block. Every sentence serves a purpose, and there is no redundant wording. It could be slightly more detailed without becoming verbose, but it is effectively concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no annotations, the description falls short. It mentions the return dictionary but omits critical details: whether the notebook file is edited in place, how filters combine (AND/OR), and behavior when no filters are specified. The presence of an output schema does not compensate for missing safety and edge-case context. More behavioral guidance is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. It does explain each parameter: filepath as path, cell_type as optional filter, pattern as optional regex. However, it does not specify what values cell_type accepts (e.g., 'code', 'markdown') or what content the pattern matches against (e.g., cell source, output). This adds meaning but leaves important semantics unspecified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Keep only cells matching criteria in a Jupyter Notebook (.ipynb), delete others.' This uses a specific verb ('keep only'/'delete') and identifies the resource (Jupyter Notebook cells). It distinguishes itself from sibling tools like delete_cell or extract_cells by describing a batch filter-and-delete operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you want to retain only cells matching certain criteria (cell_type and/or pattern) and remove the rest. However, it provides no explicit when-to-use guidance, alternatives, or exclusions. It does not mention when to prefer this over delete_cells_batch or extract_cells, leaving usage context somewhat implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It only states the return type ('Dict with 'kernels' list') and the general scope ('common' configurations). It does not confirm that the operation is read-only, side-effect-free, or how 'common' is determined, leaving important behavioral traits undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, with a single purpose sentence and a return-type sentence. No wasted words; the structure is clean and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter listing tool, the description provides the essential information: what it lists and the return shape. The output schema exists and covers return structure. A slight gap is the ambiguity of 'common' (e.g., does it include all discovered kernels or a filtered subset?), but overall it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema is empty, so no parameter descriptions are needed. The description adds no parameter info, but that is acceptable given the baseline of 4 for parameterless tools with full schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists Jupyter notebook kernel configurations, using a specific verb ('list') and resource ('kernel configurations'). It is distinguishable from sibling tools that operate on cells or metadata, though it doesn't explicitly address what 'common' means or contrast with a hypothetical 'list all' variant.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. While sibling tools like ipynb_set_kernel imply a use case (check available kernels before setting one), the description itself offers no explicit when-to-use or when-not-to-use instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the return format (dict with 'valid' boolean and optional 'errors' list), but does not mention whether validation has side effects, requires specific permissions, or how it handles malformed/nonexistent files. 'Validate' implies read-only, but this is not made explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, with a clear purpose statement followed by Args and Returns sections. Every sentence earns its place with no verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter tool with an output schema, the description is largely complete. It covers the primary purpose, parameter semantics, and return shape. However, it omits guidance on when to prefer this over the batch variant and does not address error behavior, leaving minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It does so by explaining that ipynb_filepath is a path to a .ipynb file and that an absolute path is preferred, adding meaning beyond the schema's bare string type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Validate Jupyter Notebook (.ipynb) structure.' It uses a specific verb and resource, distinguishing it from the batch sibling by its singular scope, though it does not explicitly name the alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like ipynb_validate_notebooks_batch. The description only defines what the tool does, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for disclosing behavior. It mentions the return format ('Dict with results mapping filepath to validation status') but does not clarify what validation entails, whether it makes modifications, or how errors are handled. This is minimal transparency for a tool with no safety metadata.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise and well-structured: a clear one-line purpose followed by Args and Returns sections. Every sentence provides necessary information and nothing is wasted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, output schema exists), the description provides adequate structure but lacks detail on the validation status values or error behavior. It is complete enough for a straightforward batch operation but leaves several behavioral questions unanswered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It explains the parameter 'ipynb_filepaths' as 'List of notebook paths' and adds the valuable hint 'absolute paths preferred'. This adds some meaning beyond the bare schema, though the parameter name already conveys most of the intent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb and resource: 'Validate multiple Jupyter Notebooks (.ipynb)'. The use of 'multiple' and 'batch' in the tool name distinguishes it from the sibling 'ipynb_validate_notebook', making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for validating multiple notebooks but does not explicitly state when to use this over the singular sibling alternative. There is no clear 'when-to-use' or 'use-cases' guidance beyond the general purpose, and no exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the action and return format but does not disclose that clearing outputs modifies the notebook files in place, is irreversible, or how file errors are handled. This is a significant gap for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (one purpose sentence plus Args/Returns) and front-loaded. Every sentence adds value, and the structure is easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description covers purpose, parameter format, and return shape. However, it omits important context such as in-place file mutation, potential batch partial-failure behavior, and any preconditions. Given the lack of annotations, this leaves the agent underinformed about side effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has a single parameter with 0% coverage, but the description adds meaningful details: 'Single filepath or list of filepaths' clarifies the anyOf union, and 'absolute paths preferred' gives practical guidance. This goes beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Clear' and precise resource 'all outputs from code cells in one or more Jupyter Notebooks'. It clearly distinguishes from sibling tools that target metadata, kernels, or cell editing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied by the description ('clear outputs'), but there is no explicit guidance on when to prefer this over alternatives like ipynb_apply_to_notebooks or ipynb_validate_notebooks_batch. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only mentions the return format ('Dict with 'success' or 'error' key') and does not disclose whether the file is modified in place, the validation rules for 'new_order', or potential side effects. This is insufficient for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with Args and Returns sections. It front-loads the core purpose in the first sentence and contains no filler. Every sentence contributes useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has moderate complexity: it accepts two parameters and returns a simple dict. The description covers inputs and return shape, which is adequate for a straightforward operation. However, it lacks critical context about validation, file mutation behavior, and error handling, especially since there are no annotations to fill the gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides only names and types, so the description adds valuable meaning: it explains that 'ipynb_filepath' is a path to a .ipynb file (absolute path preferred) and that 'new_order' is a list of indices using 0-based indexing. This goes beyond the schema, though it omits constraints like requiring a full permutation of cell indices.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Reorder cells') and the resource ('Jupyter Notebook (.ipynb)'), and specifies the method ('by providing new index mapping'). This makes it easy to understand what the tool does and distinguishes it from sibling cell manipulation tools like insert, delete, or replace.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used when reordering notebook cells, but it does not explicitly state when to use it versus alternatives, nor does it mention exclusions or conditions. No reference to sibling tools or scenarios where another operation would be more appropriate is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Replace' which implies overwriting, but it does not disclose that existing cell content is lost, whether cell metadata is preserved, what happens with out-of-range indices, or whether the file must already exist. It only mentions the return dict with 'success' or 'error', which is minimal. For a mutation tool with no annotation context, this transparency is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: a single sentence stating the core purpose, followed by a structured Args/Returns list. Every sentence earns its place, and the format is familiar and easy to parse. It is appropriately sized and front-loaded with the primary verb and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is a simple mutation with 3 required parameters and an output schema, so the description need not explain return values in detail. However, it lacks usage guidance and behavioral transparency (e.g., destructive nature, existence requirements). While the core operation is clear, the contextual completeness is adequate but not thorough enough to fully guide an agent in all relevant scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero description coverage (0%), so the description must compensate, and it does. Each parameter gets a meaningful comment: ipynb_filepath notes absolute path preference, cell_index identifies the cell to replace, and new_content clarifies that it should be a raw string with no additional escaping. This adds practical guidance beyond the bare parameter names and types. It does not go into deeper detail (e.g., encoding, negative indices), so a 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Replace entire cell content in a Jupyter Notebook (.ipynb).' This clearly distinguishes it from siblings like ipynb_insert_cell, ipynb_append_cell, ipynb_delete_cell, and ipynb_str_replace_in_cell, which target different actions (inserting, appending, deleting, or partial replacement). The scope is explicit and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through its core statement, but it does not explicitly state when to choose this tool over alternatives. It lacks exclusions or comparisons to sibling tools such as ipynb_str_replace_in_cell or ipynb_replace_cells_batch. The intent is clear enough for an agent to infer, but without explicit guidance, it only meets the 'implied usage' bar.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. It discloses the main action and return dict, but doesn't mention in-place modification, error cases, or cell_index validity. Some transparency, but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Concise and well-structured: a single-sentence action followed by a clear argument list and return type. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is adequate for a simple tool but lacks edge-case details (e.g., out-of-range cell_index, whether file is modified in place, exact structure of the return dict). It's more complete than a bare schema but leaves room for interpretation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has no parameter descriptions, but the description compensates by explaining each arg, including helpful guidance on raw strings and no escaping needed. This adds significant value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Replace') and resource ('substring within cell content in a Jupyter Notebook'), distinguishing it from siblings like ipynb_replace_cell (whole cell replacement) and ipynb_search_replace_all (across cells).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives (e.g., replace_cell, search_replace_all). It simply states the action without context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden. It lists parameters and return keys, but omits potential side effects (e.g., whether input notebooks are modified, whether output file is overwritten) and details about how 'pattern' matches against cell content. This leaves notable gaps in behavioral understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a one-line summary, an Args section, and a Returns section. Every sentence is useful and there is no fluff. This format is ideal for machine parsing and agent comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no annotations and a large sibling set, yet the description does not explicitly differentiate from similar operations like ipynb_filter_cells or ipynb_merge_notebooks. It also lacks critical context such as overwrite behavior and exact matching semantics. Despite having a Returns section, the overall context is incomplete for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameter descriptions (0% coverage), but the description provides meaningful explanations for all four parameters, noting optionality and path preferences. It does not specify valid cell_type values or what pattern matches against, so it doesn't fully compensate for the schema's silence, but it adds substantial value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Extract matching cells from multiple Jupyter Notebooks') and the output resource ('into a new notebook'). This distinguishes it from siblings like ipynb_merge_notebooks or ipynb_filter_cells, which operate on entire notebooks or a single notebook respectively.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for extracting filtered cells from multiple notebooks into one, but does not explicitly state when to use this tool over sibling tools or provide exclusion criteria. It leaves the agent to infer the context from the action alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the insertion behavior, 0-based indexing, that indices shift by +1, and the return structure (dict with success and new_cell_count or error). It also notes that content requires no additional escaping. This goes beyond a simple restatement and gives the agent useful operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections for Args, Returns, and Note. Each sentence adds value: the main operation is stated upfront, parameters are concisely explained, return format is specified, and the index-shifting caveat is important. There is no fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (file mutation, multiple parameters), the description covers the key aspects: operation, parameters, return value, and a behavioral nuance. It does not enumerate error scenarios beyond returning an 'error' key, nor does it discuss prerequisites, but it is sufficiently complete for an insert cell operation, especially with an output schema available.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema contains no descriptions for any of the 4 parameters (0% schema description coverage). The description compensates fully by explaining each parameter: absolute path preference for ipynb_filepath, 0-based indexing for cell_index, raw string handling for content, and allowed values for cell_type. This is excellent parameter-level guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Insert'), a specific resource ('new cell'), and the location ('at specified position' in a Jupyter Notebook file). It is clear and distinct from sibling tools like append_cell or delete_cell, though it does not explicitly name alternatives or edge cases, so a perfect score is not warranted.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives such as ipynb_append_cell or ipynb_insert_cells_batch. It does include a practical note about index shifting, which helps with usage, but it does not address tool selection or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the return structure (Dict with 'results' list or 'error' key) and clarifies that the pattern supports regex, which are useful behavioral details. It does not explicitly confirm that the operation is read-only, but the verb 'Search' strongly implies a non-destructive read operation, which is sufficient for a tool of this nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, with clear Args and Returns sections. Each sentence adds necessary information without redundancy, and the structure makes it easy for an agent to parse parameter names and expected output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool, the description covers purpose, parameters, and return type. An output schema exists, so return-value details need not be explained. The primary gap is the lack of explicit usage differentiation from sibling search tools, but overall it provides sufficient information for an agent to select and invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides semantics for all three parameters: ipynb_filepath as a path (absolute preferred), pattern as a regex-supporting search string, and case_sensitive as a flag controlling case sensitivity. Since the schema has no property descriptions (0% coverage), this description compensates well by adding meaning to each parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Search' and identifies the resource as 'cell content of a Jupyter Notebook (.ipynb)', clearly indicating a targeted search operation. It distinguishes from sibling tools like ipynb_search_replace_all by not mentioning any replacement functionality, but does not explicitly differentiate from ipynb_search_notebooks, which could also search notebook content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating it searches cell content of a single notebook, but does not explicitly state when to use this tool over alternatives like ipynb_search_notebooks (for multiple notebooks) or ipynb_search_replace_all (for search-and-replace). There are no excluded cases or alternative tool references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the return structure (results/match_count/error) and a practical note about absolute paths, but it does not explicitly confirm the tool is read-only or mention any side effects or prerequisites. Some behavioral info is present, but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a brief purpose line followed by an Args section and a Returns section. Every line provides useful information, though there is some vertical whitespace. It is concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema exists, so return values are formally defined. The description gives the purpose, all parameter explanations, and a summary of the return keys. It lacks details on edge cases or limitations, but for a search tool with an output schema, this is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description compensates by explaining each parameter: 'ipynb_filepaths' gets a clarification of absolute paths preferred, 'pattern' is identified as a regex, and 'return_context' is described as a toggle for context. This adds meaningful semantics beyond the bare schema types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search') and the resource ('multiple Jupyter Notebooks'), and the scope is distinct from siblings like ipynb_search_cells which operates on individual cells. It immediately conveys what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it (searching across notebooks), but it does not explicitly state when not to use it or mention alternatives such as ipynb_search_cells for cell-level search. The context is clear but lacks exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description discloses some behavioral details such as the shift of indices after deletion and the return dictionary. However, it does not mention whether the file is overwritten in place or any potential destructive side effects beyond the deletion itself.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with Args, Returns, and Note sections. It is efficient and each piece of information adds value, though it could be slightly more compact while retaining the same information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, the description covers inputs, outputs, and a key side effect (index shift). It mentions the error key for failures, making it reasonably complete for the operation described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no descriptions for parameters, but the description fully compensates by explaining 'ipynb_filepath' (with absolute path preference) and 'cell_index' (0-based indexing). This provides critical meaning that the schema lacks.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Delete cell at specified index') and the resource ('Jupyter Notebook (.ipynb)'). This distinguishes it from sibling tools like ipynb_delete_cells_batch by explicitly focusing on a single cell deletion at a given index.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for deleting a single cell by index but does not explicitly mention alternatives or when not to use it. There is no reference to batch deletion or other related tools, so the guidance is moderate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full burden. It discloses the return format (dict with 'cells' list or 'error' key) and that it provides content previews. However, it does not explicitly state that the operation is non-mutating, what the preview length is, or any potential performance implications for large notebooks. This leaves some gaps in behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, with the purpose stated upfront and then Args/Returns sections. It avoids unnecessary repetition and every sentence earns its place. The format is easy to scan and understand.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no nested objects, the description covers the essential information: what it does, what the parameter means, and what it returns. It could improve by adding details like whether cells are returned in order or what constitutes a 'content preview,' but overall it is complete enough for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The parameter description adds value beyond the schema, which only specifies 'type': 'string'. The description explains that ipynb_filepath is the path to the Jupyter Notebook file and adds a useful preference: 'absolute path preferred.' This provides context not available in the schema, though it could include more detail like accepted formats or examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'List all cells in a Jupyter Notebook (.ipynb) with indices, types, and content previews.' This uses a specific verb (list), identifies the resource (cells in a notebook), and specifies the output details, distinguishing it from sibling tools like ipynb_get_cell which targets a single cell.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by indicating it lists all cells, but it does not explicitly state when to use this tool over alternatives or mention any exclusions. For example, there is no guidance like 'use ipynb_get_cell for a single cell' or 'use ipynb_search_cells for filtered access.' The context is clear but basic, so a score of 3 is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the return format (dict with 'success' and 'results' or 'error') and lists allowed operations, but it lacks details on side effects (e.g., in-place file modification), error handling across multiple notebooks, or operation-specific behavior. Some transparency is present but significant gaps remain.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, well-structured with a summary line and clearly labeled Args/Returns sections. Every sentence adds necessary information without redundancy, and the formatting aids parsing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multiple operations, batch processing) and lack of annotations, the description covers the essentials but omits important context such as whether results are returned per notebook, how partial failures are handled, and whether operations modify files directly. The output schema likely covers return structure, but the behavioral details for batch execution are incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. The Args section provides meaningful semantics for all three parameters: filepaths (list, absolute preferred), operation (allowed values listed), and operation_params (dictionary). It could be more precise about the expected keys for each operation, but it adds substantial value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Apply same operation to multiple Jupyter Notebooks' with a specific verb and resource. It distinguishes from siblings by emphasizing the batch nature and listing the supported operations ('set_kernel', 'clear_outputs', 'update_metadata'), making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: it is for applying the same operation to multiple notebooks. However, it does not explicitly mention when not to use it or point to alternative sibling tools for single notebook operations, though the batch wording implies that differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the operation replaces multiple cells and returns a dict with 'success' and 'cells_modified' or 'error'. However, it does not note that replacements are destructive/overwrite existing content, nor does it mention any validation of cell indexes or handling of invalid inputs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is tightly structured with Args and Returns, each sentence contributing directly to understanding. It is brief, well-organized, and free of redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity and the presence of an output schema, the description covers the main inputs and return format. It could be more complete by specifying error cases (e.g., invalid cell index) or noting whether outputs are cleared, but it is sufficiently informative for typical batch replacement operations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description significantly enriches the schema: it explains 'ipynb_filepath' as a path with 'absolute path preferred', and 'replacements' as a list of dicts with 'cell_index' and 'content' keys, advising raw strings without escaping. This is essential because the schema itself provides no descriptions (0% coverage).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Replace multiple cells in one operation in a Jupyter Notebook (.ipynb)', using a specific verb and resource. The phrase 'multiple cells in one operation' distinguishes it from the sibling tool ipynb_replace_cell, which handles single-cell replacement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for batch replacement ('multiple cells in one operation') but does not explicitly mention when to use it over alternatives like ipynb_replace_cell or ipynb_str_replace_in_cell. It provides sufficient context for a reasonable user, but lacks explicit exclusions or alternative references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the splitting criteria (markdown_headers vs cell_count), the output directory, and the return format (success/files_created/error). However, it fails to mention whether the original file is modified, if existing files are overwritten, how output files are named, or any permission requirements. This is partial transparency but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized: a one-sentence purpose, followed by a compact Args list and a Returns note. It is front-loaded with the main action and every sentence serves a purpose. No fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters and moderate complexity, the description covers all parameters and the return value. It lacks some operational details such as output file naming, overwrite behavior, and specific error conditions beyond returning an 'error' key. However, the core functionality is well specified, making it mostly complete for the intended use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully explain parameters. It does so clearly: ipynb_filepath (path, absolute preferred), output_dir (directory for output), and split_by (allowed values 'markdown_headers' or 'cell_count'). This adds meaning beyond the bare schema and fully compensates for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Split a Jupyter Notebook (.ipynb) into multiple files by criteria.' It uses a specific verb (split) and resource (Jupyter Notebook), and the criteria are further detailed via the split_by parameter. This distinguishes it from sibling tools like merge_notebooks or extract_cells, which perform different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when you need to split a notebook by markdown headers or cell count) but does not explicitly discuss alternatives or exclusion cases. It provides no guidance on when not to use it or how it compares to related tools like extract_cells or apply_to_notebooks, so it only meets the 'implied usage' level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses return shape ('Dict with content or error key'), supports negative indexing, and notes absolute paths are preferred. It does not explicitly state it is read-only, but 'Get content' clearly implies no mutation, and error behavior is covered by the 'error' key.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, uses a clear Args/Returns structure, and front-loads the main purpose. Every line adds useful information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter getter, the description is largely complete: it explains inputs, supports negative indexing, and indicates return/error keys. Since an output schema exists (per context), return values don't need additional explanation. Minor gaps like zero-based indexing or invalid-index behavior are not critical.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description provides the needed semantics. It explains ipynb_filepath as the path (absolute preferred) and cell_index as supporting negative indexing. This adds value beyond the raw schema, though it doesn't clarify zero-based indexing or exact error conditions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get content of a specific cell by index from a Jupyter Notebook (.ipynb)', specifying the verb, resource, and lookup method. This distinguishes it from sibling tools like ipynb_list_cells (lists cells) and ipynb_get_metadata (gets metadata).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The usage is implied: use when you need content of a specific cell by index. However, there is no explicit guidance on when not to use it or which alternative tool might be better suited, such as ipynb_search_cells for searching by content.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does explain the return value ('success' and 'replacements_made' or 'error') and notes that 'pattern' is regex, but it does not explicitly state that the notebook file is modified in place, whether changes are reversible, or potential error conditions beyond the generic 'error' key.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a one-sentence purpose, a straightforward Args list, and a Returns line. It front-loads the core behavior and avoids unnecessary filler, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no annotations, no schema descriptions), the description covers the key essentials: purpose, parameters, and return behavior. It lacks explicit mutation warnings or detailed error semantics, but it provides enough for a competent agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides zero descriptions (0% coverage), but the description's Args section compensates by explaining each parameter: the filepath with 'absolute path preferred', pattern as 'regex', replacement as 'replacement string', and cell_type as 'Optional filter by cell type'. This adds meaningful semantics beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Search and replace across all cells in a Jupyter Notebook (.ipynb).' The verb+resource combination is specific, and the scope 'across all cells' distinguishes it from sibling tools like ipynb_replace_cell (single cell) and ipynb_search_cells (search only).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool via the phrase 'across all cells' and the optional 'cell_type' filter, which signals global scope. However, it does not explicitly name alternatives or state when not to use it, making it a clear but not fully explicit usage guide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden of disclosing behavior. It does state the return value (dict with 'success' and 'cell_index') and a key nuance (content is a raw string, no escaping needed). However, it does not disclose side effects such as whether the file is written immediately, whether existing notebook content is preserved (though appending implies it), or error conditions like file-not-found. This is a moderate level of transparency, hence a 3.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact yet complete: a one-sentence purpose, followed by concise parameter and return-value explanations. The structured Args/Returns format improves readability without wasting words. Every sentence contributes value, and the primary action is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple mutation tool with 3 parameters, the description covers all parameters, explains the return value (with the output schema further clarifying), and includes a usage nuance. It does not mention file save behavior or error handling, but given the simplicity and the presence of an output schema, it is nearly complete. A score of 4 acknowledges the small gaps while recognizing the overall sufficiency.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero descriptions (0% coverage), so the description must fully compensate. The Args section explains each parameter: the file path (absolute preferred), content (raw string, no escaping), and cell_type (allowed values 'code', 'markdown', 'raw'). This adds meaningful semantic information beyond the bare schema, making this dimension a 5.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb+resource: 'Append cell to end of a Jupyter Notebook (.ipynb).' This clearly distinguishes it from sibling tools like ipynb_insert_cell (which inserts at a position) and ipynb_replace_cell (which replaces existing cells).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly conveys the intended use case: adding a cell to the very end of a notebook. It does not list alternatives or exclusions, but the context is unambiguous enough that an agent would know when to choose this over insert_cell or replace_cell. Score 4 reflects clear context without explicit when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the non-obvious descending-order deletion processing to maintain index validity, instructs that indices should be as they appear before any deletions, and lists the return keys ('success', 'cells_deleted', 'new_cell_count'). This adds significant context beyond obvious deletion behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections: summary, Args, Returns, and Note. It is concise, front-loaded with the action, and every sentence contributes useful information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is nearly complete given the presence of an output schema and detailed parameter explanations. It covers the critical non-obvious detail of descending-order processing. It could potentially mention that the file is modified on disk and that deletions are irreversible, but these are inferable from the tool's purpose. Overall, it is adequate for a batch deletion tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, but the description fully compensates with an Args section explaining both parameters: ipynb_filepath (path to .ipynb, absolute path preferred) and cell_indices (list of 0-based indices). This adds meaning beyond the bare type definitions in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Delete multiple cells by indices from a Jupyter Notebook (.ipynb).' This is a specific verb+resource construction, and the emphasis on 'multiple cells' distinguishes it from the singular ipynb_delete_cell sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit comparison to alternatives or guidance on when to use this tool over others. The name and phrase 'multiple cells' imply batch usage, and the note about descending order is a usage constraint, but there is no direct exclusion or alternative-referencing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates the return type (metadata dictionary) and the possibility of an 'error' key, which hints at error behavior. The read-only nature is implied by 'Get', and the cell_index semantics are explained. However, edge cases like invalid index or missing file are not detailed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a one-line summary followed by Args and Returns sections. It is concise, front-loaded with the purpose, and every line adds value without unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple with two parameters, and the description explains both plus the return behavior. An output schema exists, so detailed return keys are not needed. Minor omissions like whether cell_index is zero-based prevent a perfect score, but overall the context is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides only type and default, but the description adds significant meaning: 'absolute path preferred' for ipynb_filepath and '(None for notebook metadata)' for cell_index. This fully compensates for the 0% schema description coverage and gives the agent precise usage info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves Jupyter Notebook or cell metadata, using a specific verb ('Get') and resource. It distinguishes from sibling tools like ipynb_get_cell that retrieve cell content rather than metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The usage is implied: use this tool to fetch metadata from a notebook or cell. However, it does not explicitly mention when to use this over alternatives such as ipynb_get_notebook_info or ipynb_get_cell, nor does it provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of disclosing behavior. It specifies the return dictionary structure, including an 'error' key, which signals failure handling. It also notes 'absolute path preferred', providing practical context. However, it does not explicitly state read-only behavior or discuss side effects, though the verb 'Get' strongly implies non-mutation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with 'Args:' and 'Returns:' sections. It front-loads the purpose in the first sentence and contains no unnecessary words or repetition. Every element earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only info tool with one parameter and an output schema, the description is largely complete. It covers the purpose, parameter semantics, and return value shape. Minor gaps include not specifying units for file_size or detailing error conditions beyond the 'error' key, but these are not critical for a tool of this simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% for the single parameter, and the description fully compensates by explaining what `ipynb_filepath` is and adding a usage hint ('absolute path preferred'). This goes beyond the bare schema type and provides meaningful guidance for correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb+resource: 'Get summary information about a Jupyter Notebook (.ipynb)'. It is specific and distinct from siblings like ipynb_read_notebook (full content) and ipynb_get_metadata (metadata only), as it lists specific summary fields (cell_count, cell_types, kernel, format_version, file_size).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly indicates usage for obtaining a high-level overview, but it does not provide explicit guidance on when to use this tool versus alternatives like ipynb_read_notebook or ipynb_get_metadata. No exclusions or alternative mentions are present, so usage is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It explicitly reveals 0-based indexing, the effect of processing order on indices, and suggests sorting descending to maintain intended positions. It also describes the return dict, which adds transparency. It does not mention file system side effects or permissions, but the core behavioral quirks are well documented.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is organized into Args, Returns, and Note sections, each concise and front-loaded. Every sentence adds value, and the note is a single actionable tip. No waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a batch tool with multiple parameters, the description covers the essential aspects: purpose, both parameters, return format, and the ordering pitfall. It does not delve into edge cases like out-of-range indices, but the presence of an output schema reduces the need to document return values. Overall, it is sufficient for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description is essential. It explains ipynb_filepath as a path with absolute path preference, and describes insertions as a list of dicts with 'cell_index', 'content', and 'cell_type' keys, adding the crucial detail that content should be raw strings. This fully compensates for the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with 'Insert multiple cells at specified positions in a Jupyter Notebook (.ipynb)', which uses a specific verb and resource, and clearly distinguishes this batch tool from its singular sibling ipynb_insert_cell. It precisely communicates the scope of the operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for batch insertion by saying 'multiple cells', but it does not explicitly compare with alternatives or state when not to use this tool. The note about sorting indices is a parameter-level guidance, not tool-selection guidance, so it only partially addresses when/how to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the return value (dict with success/total_cells/notebooks_merged/error) and the add_separators option, but lacks important behavioral details such as whether the output file is overwritten, whether input notebooks are modified, or how cells/metadata are handled. With no annotations provided, the description carries the full burden for safety disclosures, and this gap prevents a higher score.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a one-sentence purpose followed by an Args/Returns breakdown. Every sentence is informative, and there is no fluff or repetition. The front-loaded purpose aids quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 3 parameters, all of which are described, and the return value is specified. For a merge operation, this is nearly complete. However, edge cases like handling an existing output file or empty input lists are not addressed, leaving a small gap in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description fully compensates by explaining each parameter: output_ipynb_filepath (path for merged notebook), input_ipynb_filepaths (list of notebook paths), and add_separators (whether to add separator cells). This adds meaning far beyond the raw schema, making parameter semantics excellent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Merge multiple Jupyter Notebooks (.ipynb) into one.' This specific verb+resource combination immediately distinguishes it from siblings like ipynb_split_notebook (which splits) and ipynb_apply_to_notebooks (which applies operations). No ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes the use case obvious: when you need to combine multiple notebooks into a single one. It does not explicitly mention alternatives or when not to use it, but the context is clear. Without explicit exclusions or mentions of sibling tools, it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full responsibility. It discloses the return format (dict with cell_count, cell_types, kernel_info, format_version), mentions the error key on failure, and notes the preference for absolute paths. This is solid transparency for a read-only tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with Args and Returns sections. Every sentence provides value, no fluff, and the key information is front-loaded in the first sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read tool with an output schema, the description covers the essential aspects: purpose, parameter meaning, return structure, and failure behavior. It is complete and self-contained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description compensates by explaining 'ipynb_filepath' as the path to a Jupyter Notebook file and recommends an absolute path. This adds meaningful context beyond the schema's bare string type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it reads a Jupyter Notebook and returns a structure summary. The verb 'Read' and resource 'Jupyter Notebook (.ipynb)' are specific, and the summary output distinguishes it from sibling tools focused on editing or cell-level operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when a structure summary is needed, but it does not explicitly mention alternatives or when not to use it. With many sibling tools like ipynb_get_notebook_info or ipynb_list_cells, it would benefit from explicit differentiation, but the context is clear enough for basic selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since there are no annotations, the description carries the full burden. It discloses that the 'metadata' dictionary is 'merge'd, implying it doesn't replace the entire metadata but merges fields. It also states the return format (Dict with 'success' or 'error' key). However, it omits side-effect details such as in-place file modification, permissions required, or whether the change is reversible. It provides some useful behavior but not a comprehensive disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is tightly structured as a docstring with a one-line purpose followed by Args and Returns sections. Each sentence provides essential information without fluff. The format is easy to parse, and all three parameters are explained in a consistent, compact way.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool of moderate complexity (3 parameters, output schema indicated), the description covers the key aspects: what it does, parameter meanings, and return shape. The presence of many sibling tools means the description could more explicitly differentiate from ipynb_sync_metadata or ipynb_replace_cell, but the core usage is well-specified. It lacks edge-case notes (e.g., error conditions) but is generally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully explain each parameter. It does: 'ipynb_filepath' is described as the path to the .ipynb file (absolute preferred), 'metadata' is the dictionary to merge, and 'cell_index' is the index of the cell (None for notebook metadata). This goes far beyond the raw schema types and gives meaningful semantics for correct usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action: 'Update Jupyter Notebook (.ipynb) or cell metadata.' This is a specific verb-resource combination that distinguishes it from sibling tools like ipynb_get_metadata (read) and ipynb_sync_metadata (sync). The mention of cell_index clarifies the two possible target scopes (notebook vs. cell), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool: updating metadata (either notebook-level or cell-level via cell_index). It explains that using cell_index=None targets notebook metadata, while a numeric cell_index targets a specific cell. However, it does not explicitly mention alternatives or when not to use it, but the implied usage from the description is sufficient for selecting it over read-only or other mutation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/jsamuel1/jupyter-editor-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server